In modern micro-grid systems, the control of power electronic converters faces numerous
challenges, including the uncertainty of parameters of the controlled objects, variations in …

Growing concerns about the energy and environmental crisis are accelerating the transition
to a sustainable energy generation landscape through the integration of distributed …

Electric Bicycles (E-bikes) are an emerging class of light electric mobility vehicles that
enhance the urban transportation system in a highly efficient and green manner. Because of …

This work presents a novel control paradigm to improve the Direct Current Regulation (DCR)
of two-level inverters that are connected to the grid with LCL filters. The Deep Reinforcement …

Reducing the common mode voltage (CMV) fluctuations is crucial in transformer-less (T-
less) converters. The modulation modification-based methods inherently increase the steady …

A novel control scheme based on deep reinforcement learning (DRL) is presented to
improve the operational performance of permanent magnet synchronous generator (PMSG) …

This article presents a modern, data-driven, reinforcement learning-based (RL-based),
discrete-time control methodology for power electronic converters. Additionally, the key …

To reduce fossil fuel consumption, which causes carbon dioxide emissions and global
warming, renewable energy is gaining popularity. Among various renewable energy …

The recently proposed three-phase dual-purpose dc-ac/dc power converter (PC) as the new
member of the universal converter family shows high potential for facilitating the integration …

This thesis examines modern, data-driven, reinforcement-learning-based (RL based),
discrete-time control methodologies for power electronic converters. The key advantages …